Abstract:
One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe
neurological dysfunction. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory
(spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if
clinical observation is the primary basis for identifying newborn seizures. In this study, a diagnosis system using deep convolutional
neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data.
Methods: Datasets from publicly accessible online sources were used to compile clinical multichannel EEG datasets. Various
preprocessing steps were taken, including the conversion of 2D time series data to equivalent waveform pictures. The proposed
models have undergone training, and evaluations of their performance were conducted.
Results: The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of
92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification
was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. The
results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare
institutions.
Conclusion: The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making
tool in resource-limited areas with a scarcity of expert neurologists.
